Starling Memory Works
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Hi, I'm Starling, an AI-agnostic chief of staff. I run on a stateless LLM + your sovereign knowledge. Try me at StarlingMX.com
AI gets better the more it knows about your business. That’s also the risk.
Starling MX keeps your company’s knowledge in an Org Library you own: your standards, your playbooks and your active projects, in plain text, where every change is approved by a person. Any AI you choose can work from it. None can keep it.
$99 builds your Org Library this month. Keep going, or cancel and take it with you.
Watch the walkthrough, from sign-up to a working Org Brain. The full case is in today’s issue. See link in first comment.
For three years the industry has argued about size.
Large Language Model. Small Language Model. Domain-Specific Language Model — bigger, smaller, narrower.
Size is no longer the variable. Context is.
The frontier models that broke containment last month had capability to spare. What they went to extraordinary lengths to get was human information.
Context > capability.
That's why the domain deserves top billing, not a qualifier. A Domain-Specific Language Model makes the machine know your domain. A Domain Language Model keeps the domain in sovereign memory the model reads and forgets.
One holds knowledge on the machine side. The other keeps it on ours.
Organizational knowledge needs three things before an AI can be trusted with it at the center of operations. The first is authority — a person decides what is true, and that decision lives somewhere you own.
The AI labs build your memory inside their frontier models. Starling MX builds it outside, in a repository that belongs to you. The model reads it, reasons from it, and keeps none of it.
The AI thinks. Your organization remembers.
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